Showing papers by "William W. Cooper published in 1999"

Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA-Solver Software

Abstract: List of Tables. List of Figures. Preface. 1. General Discussion. 2. The Basic CCR Model. 3. The CCR Model and Production Correspondence. 4. Alternative DEA Models. 5. Returns to Scale. 6. Models with Restricted Multipliers. 7. Discretionary, Non-Discretionary and Categorical Variables. 8. Allocation Models. 9. Data Variations. Appendices. Index.

RAM: A Range Adjusted Measure of Inefficiency for Use with Additive Models, and Relations to Other Models and Measures in DEA

Abstract: Generalized Efficiency Measures (GEMS) for use in DEA are developed and analyzed in a context of differing models where they might be employed. The additive model of DEA is accorded a central role and developed in association with a new measure of efficiency referred to as RAM (Range Adjusted Measure). The need for separately treating input oriented and output oriented approaches to efficient measurement is eliminated because additive models effect their evaluations by maximizing distance from the efficient frontier (in l1, or weighted l1, measure) and thereby simultaneously maximize outputs and minimize inputs. Contacts with other models and approaches are maintained with theorems and accompanying proofs to ensure the validity of the thus identified relations. New criteria are supplied, both managerial and mathematical, for evaluating proposed measures. The concept of “approximating models” is used to further extend these possibilities. The focus of the paper is on the “physical” aspects of performance involved in “technical” and “mix” inefficiencies. However, an Appendix shows how “overall,” “allocative” and “technical” inefficiencies may be incorporated in additive models.

Idea and Ar-Idea: Models for Dealing with Imprecise Data in Dea

Abstract: Data Envelopment Analysis (DEA) is a nonparametric approach to evaluating the relative efficiency of decision making units (DMUs) that use multiple inputs to produce multiple outputs An assumption underlying DEA is that all the data assume the form of specific numerical values In some applications, however, the data may be imprecise For instance, some of the data may be known only within specified bounds, while other data may be known only in terms of ordinal relations DEA with imprecise data or, more compactly, the Imprecise Data Envelopment Analysis (IDEA) method developed in this paper permits mixtures of imprecisely- and exactly-known data, which the IDEA models transform into ordinary linear programming forms This is carried even further in the present paper to comprehend the now extensively employed Assurance Region (AR) concepts in which bounds are placed on the variables rather than the data We refer to this approach as AR-IDEA, because it replaces conditions on the variables with transformations of the data and thus also aligns the developments we describe in this paper with what are known as cone-ratio envelopments in DEA As a result, one unified approach, referred to as the AR-IDEA model, is achieved which includes not only imprecise data capabilities but also assurance region and cone-ratio envelopment concepts

Can research be basic and applied? You bet. It better be for B-schools!

Abstract: This article reviews some of the very severe criticisms of university education and research that are now being made by businessmen and government officials as well as by distinguished members of the academy such as Peter Drucker and Milton Friedman. The focus is on business schools, and, in particular, business schools in research universities. One part of the response suggested in this paper can be described as “applications driven theory”, meaning that relevance is to be attained by starting with a concrete problem in the context of an actual application. This is what we mean by “applications driven”. “Theory” becomes a recognizable part of such a research effort when the approaches used are generalized and made publicly available (e.g., by publication) for interest, and use, by others with sufficient rigor and precision to admit of validation by “third parties”. Classroom relevance is attained by addressing the need for bringing research and its management uses into the classroom. This is also to be done in a manner that will help and encourage students to learn to use the university as a resource in their subsequent careers. New organizational forms for accomplishing this include what are sometimes referred to as the “core and cloud organization forms” described in this paper. Uses of this form with accompanying examples are suggested for delivering education and effecting the kind of applications-driven theory that management schools will then be better able to undertake. An Addendum compares US higher education with the situations in other countries.

The Blackett Memorial Lecture 18 November 1997

Abstract: It is argued that movement toward the ‘control’ aspects of management should be effected to expand OR/MS activities beyond their present (almost exclusive) emphasis on ‘planning’ functions. This will involve increasing the amount of empirical-inferential approaches to research (based on ex-post data) as one way to help bring this about. Examples of such empirical-inferential research activities in OR/MS are supplied by reference to the early (founding) work of PMS Blackett and others on high-level policy problems during World War II. Extensions to the control function are evidenced in the work of Robert Fetter and others in the development of Diagnostic Related Groups on which the Prospective Payment Systems are based with large and important impacts on the health care delivery systems in the USA as well as other countries. Other examples covered include the use of OR concepts in the early ‘turnaround’ of Federal Express from failure to success. Other new methods will nevertheless be needed to extend and improve presently available OR/MS approaches. Some of these possibilities are illustrated with Data Envelopment Analysis. Ways to combine DEA with commonly used statistical methods are described to show how new and old methods may be combined to further enhance their power and range. Similar relations of DEA to other OR/MS approaches are now being studied, some of which are presently occurring with ‘fuzzy sets’ and ‘multiple objective programming.’